Multi-Sensor Fusion Localization of Indoor Mobile Robot
Bibliographic record
Abstract
Localization is critical for map building in visual SLAM (Simultaneous Localization and Map). Currently, accurate localization systems, such as Motion Capture, are expensive and, as many of them, not easy for re-configuration, a property essential for field robot test. This paper proposes a camera-odometry fusion method, which bases on a camera-marker system of low-cost and easy for re-configuration. The technology is based on odometers by combining two different sensor modules and PES using EKF (Extended Kalman Filter). A critical problem of EKF is the unknown PES (Position Estiamte System) variance, which is always set as a constant in previous works. In this paper, we solve this problem by using PES marker-pair, instead of a solo marker, to directly estimate the variance of PES localization. Experimental results in indoor environment demonstrate that the proposed approach substantially improves the localization accuracy of SLAM compared with PES only and odometry only. The position error is found to be less than 40mm of our system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".